Bibliographic record
Abstract
When reaching to objects, our hand and arm rarely collide with non-target objects, even if our workspace is cluttered. The simplicity of these actions hides what must be a relatively sophisticated obstacle avoidance system. Recent studies on patients with optic ataxia and visual form agnosia have demonstrated that obstacle avoidance is an automatic process, likely governed by the dorsal stream (Schindler et al., 2004, Nature Neuroscience, 7(7):779–784, Rice et al., 2006, Experimental Brain Research, 174(1):176–188). The current study sought to quantify how normal participants react to changes in the size and position of non-target objects in and around their workspace. In the first experiment, 13 right-handed subjects performed reaches to a target strip in the presence of two non-target objects, which varied in depth and horizontal configuration. We found that objects with horizontal alignments that were asymmetric about midline created systematic deviations in reach trajectory away from midline, with participants seeming to maximize the distance away from the two objects. These deviations were significantly greater for objects nearer in depth and nearly disappeared when the objects were placed beyond the target strip, suggesting the obstacle avoidance system is sensitive to the depth at which an object becomes an obstacle. In a second experiment, we varied the height of the two objects, as well as the depth. Object pairs could now be both tall, both short, or one short and one tall (with the tall on the right or on the left). We replicated the first experiment, extending the finding to include sensitivity to the size of the objects. Here the deviations induced by short objects, while still significant, were significantly less than the tall-object deviations. Taken together, these experiments indicate a sophisticated obstacle avoidance system that is extremely sensitive and conservative in evaluating potential obstacles and deviating reach accordingly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.031 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".